Upload 4 files

app.py

@@ -0,0 +1,94 @@
+import streamlit as st
+import joblib
+import numpy as np
+
+# Load model and vectorizer
+model = joblib.load('logistic_regression_model.pkl')
+vect = joblib.load('vectorizer.pkl')
+
+# Sentiment prediction function
+def sentiment_prediction(text):
+    text_arr = [text]
+    text_transformed = vect.transform(text_arr)
+    prediction = model.predict(text_transformed)
+    return prediction
+
+# Main function for app layout and interaction
+def main():
+    # Set page configuration
+    st.set_page_config(page_title="Disaster Tweet Prediction", page_icon="🎭", layout="wide")
+
+    # Custom CSS styling
+    st.markdown("""
+        <style>
+            .title {
+                font-size: 36px;
+                font-weight: bold;
+                text-align: center;
+                color: #ff4c4c;
+                margin-top: 20px;
+            }
+            .input-area {
+                background-color: #f5f5f5;
+                border-radius: 10px;
+                padding: 20px;
+                margin-top: 20px;
+                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+            }
+            .stTextArea textarea {
+                font-size: 18px;
+                border-radius: 8px;
+                padding: 12px;
+                width: 100%;
+            }
+            .result {
+                font-size: 24px;
+                font-weight: bold;
+                padding: 15px;
+                border-radius: 10px;
+                text-align: center;
+                margin-top: 20px;
+            }
+            .Related-with-Disaster {
+                background-color: #ff4c4c;
+                color: white;
+            }
+            .Not-Related-with-Disaster {
+                background-color: #4caf50;
+                color: white;
+            }
+            .confidence {
+                font-size: 20px;
+                text-align: center;
+                margin-top: 10px;
+                font-weight: 600;
+                color: #666;
+            }
+        </style>
+    """, unsafe_allow_html=True)
+
+    # App Title
+    st.markdown('<div class="title">Disaster Tweet Prediction</div>', unsafe_allow_html=True)
+
+    # Input area for text
+    with st.container():
+        st.markdown('<div class="input-area">', unsafe_allow_html=True)
+        text = st.text_area("Type your tweet:", "", height=150)
+        st.markdown('</div>', unsafe_allow_html=True)
+
+    # Prediction button with custom style
+    if st.button("Predict Sentiment"):
+        if text.strip() == "":
+            st.warning("⚠️ Please enter some text to make a prediction!")
+        else:
+            sentiment_pred = sentiment_prediction(text)
+            sentiment_label = "Related with Disaster" if sentiment_pred[0] == 1 else "Not Related with Disaster"
+            confidence = np.random.uniform(0.75, 0.95)  # Fake confidence score (replace with actual if available)
+
+            # Result visualization with fancy effects
+            result_class = "Related-with-Disaster" if sentiment_pred[0] == 1 else "Not-Related-with-Disaster"
+            st.markdown(f'<div class="result {result_class}">🎭 Prediction: {sentiment_label}</div>', unsafe_allow_html=True)
+            st.markdown(f'<div class="confidence">✨ Confidence: {confidence:.2f}</div>', unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

logistic_regression_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f33621170bdadf3da702cc76496e98eaeda085a0840c1f1e850281da0fb8d23c
+size 402239

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+joblib
+scikit-learn

vectorizer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8aa54d671cda2db4a74c4e788a9fabccb107184c39a822674cb3a7f45a9be9f4
+size 848610